The present application relates to information management, and more particularly, to technologies for information identification and quantification in natural language contents, and classification, ranking, searching, and retrieval of such contents, as well as machine-learning technologies for identifying associations between terms or symbols in such contents.
In the information age, more and more individuals and organizations are faced with the problem of information overload. Accurate and efficient methods for information access, including collection, storage, organization, search and retrieval are the key to the success in this information age.
Much of the information is contained in natural language contents, such as text documents. Various theoretical and practical attempts have been made to efficiently organize and determine the amount and relevancy of the information in natural language contents. The existing techniques, including various search engines and document classification systems, however, are often not sufficiently accurate in identifying the information focus in the content, thus often cannot effectively serve the information needs of their users. There is still a need for accurate, efficient, and automated technologies to identify, search, rank, and classify large amounts of natural language contents based on the meaning of the contents, and the amount of information they contain.